Method for detecting tumor by image analysis, device using method, and non-transitory storage medium

ABSTRACT

A method for detecting a tumor from images which are required to be shrunken in resolution obtains one or more first images. Then, the method segments or divides the detection images into a number of detection image blocks according to an input size of training data of a convolutional neural network architecture, before segmenting, each of the plurality of detection image blocks comprising coordinate values. The detection image blocks are input into a preset tumor detection model to generate image blocks of a result of the detection images. The method merges the image blocks into a single image according to the coordinate values of each detection image block. Colors of normal areas, abnormal areas, and overlapping areas of the abnormal areas are all different. The method generates a final detection according to color depths in the image. A tumor detection device and a non-transitory storage medium are provided.

FIELD

The subject matter herein generally relates to image processingtechnology, and particularly, to a tumor detecting device and method,and a non-transitory storage medium.

BACKGROUND

Limitations on an input size of training data of a convolutional neuralnetwork algorithm model are generally needed, if images with highresolution, for example 5000*4000 pixels, are directly input as thetraining data, otherwise the time of the training may become longer andthe efficiency of the training lowered. Thus images with high resolutionmust be resized to the input size of the training data and thenanalyzed. However, the resized image may lose details due to theshrinking, thus the tumor detecting method in the prior art may be ableto detect the existence of a tumor according to the image, but cannotaccurately know the location and other details of the tumor. Suchlow-resolution images may not be useful to the health-care professionalsto further process the tumor.

BRIEF DESCRIPTION OF THE DRAWINGS

Implementations of the present technology will now be described, by wayof embodiment, with reference to the attached figures.

FIG. 1 illustrates a block view of an embodiment of a tumor detectingdevice.

FIG. 2 illustrates a block view of a tumor detecting system in anembodiment of the tumor detecting device of FIG. 1.

FIG. 3 illustrates a flowchart of an embodiment of a method for creatingmodel for tumor detection.

FIG. 4 illustrates a flowchart of an embodiment of a tumor detectingmethod.

FIG. 5 illustrates a view showing the merging of a number of imageblocks into an image of result of detection.

DETAILED DESCRIPTION

It will be appreciated that for simplicity and clarity of illustration,where appropriate, reference numerals have been repeated among thedifferent figures to indicate corresponding or analogous elements. Inaddition, numerous specific details are set forth in order to provide athorough understanding of the embodiments described herein. However, itwill be understood by those of ordinary skill in the art that theembodiments described herein can be practiced without these specificdetails. In other instances, methods, procedures, and components havenot been described in detail so as not to obscure the related relevantfeature being described. The drawings are not necessarily to scale andthe proportions of certain parts can be exaggerated to better illustratedetails and features. The description is not to be considered aslimiting the scope of the embodiments described herein.

Several definitions that apply throughout this disclosure will now bepresented.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,written in a programming language. The software instructions in themodules can be embedded in firmware, such as in an erasable programmableread-only memory (EPROM) device. The modules described herein can beimplemented as either software and/or hardware modules and can be storedin any type of computer-readable medium or other storage device.

The term “comprising” means “including, but not necessarily limited to”;it specifically indicates open-ended inclusion or membership in aso-described combination, group, series, and the like.

Embodiments of the present disclosure will be described with referenceto the accompanying drawings.

FIG. 1 illustrates a block view of an embodiment of a tumor detectingdevice. The tumor detecting device 100 is configured to detect tumor inlung and accurately determine the location of the tumor according to theimage.

It can be understood that, the lung tumor can be only taken as anexample, a lung tumor is not to be considered as limiting the scope ofthe embodiments described herein. The tumor is not limited to the lungtumor, the tumor can be other types of tumor, such as heart tumor, livertumor, stomach tumor, brain tumor, kidney tumor, organs tumor, or thelike.

The tumor detecting device 100 includes a storage unit 10, at least oneprocessor 20, and a display unit 30. The display unit 30 and the storageunit 10 are coupled to the at least one processor 20.

The storage unit 10 is configured to store various data of the tumordetecting device 100, for example one or more historical images, one ormore detection images, one or more results of detection, and so on. Inthe embodiment, the storage unit 10 can include, but is not limited to,the read-only memory, random access memory, programmable read-onlymemory, erasable programmable read-only memory, one-time programmableread-only memory, electrically-erasable programmable read-only memory,compact disc read-only memory, or other optical disc memory, diskmemory, tape memory, or any other medium capable of carrying or storingthe program codes.

The at least one processor 20 can be central processing unit,microprocessor, digital processing chip, or any other processing chipcapable of executing the data processing function.

The display unit 30 is configured to display image of result ofdetection of the tumor detecting device 100, or the like. In theembodiment, the display unit 30 can be any display, for example a touchdisplay, a liquid crystal display, or the like.

Referring also to FIG. 2, a tumor detecting system 6 is run on the tumordetecting device 100. FIG. 2 illustrates a block view of an embodimentof the tumor detecting system of the tumor detecting device. In theembodiment, the tumor detecting system 6 includes a series of programinstructions in the form of procedures. The program instructions in theform of procedures are stored in the storage unit 10 and executed by theat least one processor 20 to accomplish the object of the presentdisclosure. The tumor detecting system 6 can include an image obtainingmodule 61, a segmentation module 62, a deletion module 63, apreprocessing module 64, a training module 65, a detection module 66,and a merging module 67.

The image obtaining module 61 is configured to obtain an image. Theimage can be a historical image or a detection image.

In the embodiment, the image can be a thin-section image with highresolution of the lung. It can be understood that the image is not solimited, the image can be a thin-section image with high resolution ofthe heart, the liver, the stomach, the brain, the kidney, or the organs.

The segmentation module 62 is configured to segment the historical imageinto a number of historical image blocks according to the input size ofthe training data of the convolutional neural network architecture. Thesegmentation module 62 is further configured to segment the detectionimage into a number of detection image blocks satisfying the input sizeof the training data according to the input size of the training data ofthe convolutional neural network architecture, before segmenting, eachdetection image block comprising coordinate values, each detection imageblock partially overlapping with one or more of adjacent detection imageblocks. In the embodiment, the segmentation module 62 is furtherconfigured to determine the coordinate values of each detection imageblock before segmenting.

In the embodiment, each detection image block is square. Each detectionimage block can be any other suitable shape, for example rectangle, orthe like. Each detection image block overlaps a half size of alongitudinally adjacent one of the detection image blocks and a halfsize of a widthwise adjacent one of the detection image blocks, and aquarter size of a diagonally adjacent one of the detection image blocks.

It can be understood that, in other embodiments, each detection imageblock overlaps a quarter size of the longitudinally adjacent one of thedetection image blocks or the widthwise adjacent one of the detectionimage blocks.

The deletion module 63 is configured to delete one or more historicalimage blocks comprising background of more than 50% of an entirehistorical image block segmented from the historical image.

The preprocessing module 64 is configured to expand a scale of thehistorical image block based on an image augmentation technology. Theimage augmentation technology can be at least one of a group consistingof image distortion, image flipping, image color and space changing, andimage transformation.

In the embodiment, the image augmentation technology marks a series ofrandom changes to the training images to produce similar but differenttraining examples, to expand the scale of the training data.

The training module 65 is configured to train the convolutional neuralnetwork architecture with the historical image blocks to create a tumordetection model. The convolutional neural network architecture includes,but is not limited to ResNet, AlexNet, VGG, Inception, and so on.

The detection module 66 is configured to input the detection imageblocks to the tumor detection model to generate the image blocks of aresult of the detection image.

The merging module 67 is configured to merge the image blocks of theresult of detection image into a single image according to thecoordinate values of each detection image block.

In the embodiment, the merging module 67 is further configured to judgeand generate a result of detection according to the color depths of theimage blocks in the single image displayed on the display unit.

For example, in the embodiment, in the single image, white is normal,gray is abnormal, and grayscale color depth represents a gradient ofinfection of the lung tumor.

FIG. 3 illustrates a flowchart of an embodiment of a method for creatingtumor detection model. The method for creating tumor detection model canbegin at block S301.

At block S301, obtaining one or more historical images.

In the embodiment, the historical images can be thin-section images withhigh resolution of the lung. It can be understood that the historicalimages are not limited to such images, the historical images can bethin-section images with high resolution of kidney, stomach, or thelike.

At block S302, segmenting the historical images into a number ofhistorical image blocks according to the input size of the training dataof a convolutional neural network architecture.

In the embodiment, the method includes segmenting the historical imagesinto a number of historical image blocks satisfying the input size ofthe training data according to the input size of the training data ofthe convolutional neural network architecture.

At block S303, deleting one or more historical image blocks comprisingbackgrounds of more than 50% of an entire historical image block.

In the embodiment, the method is not limited to deleting one or morehistorical image blocks with such backgrounds, the method can delete oneor more historical image blocks comprising backgrounds of more than 60%of the entire historical image block, or the like.

At block S304, expanding a scale of the historical image blocks based onthe image augmentation technology. The image augmentation technology canbe at least one of a group of image distortion, image flipping, imagecolor and space changing, and image transformation.

In the embodiment, the method expands a scale of the historical imageblocks of the training data based on the image augmentation technology.The image augmentation technology can be at least one of a group ofhistorical image block distortion, historical image block flipping,historical image block color and space changing, and historical imageblock transformation.

In the embodiment, the image augmentation technology marks a series ofrandom changes to the training images to produce similar but differenttraining examples, to expand the scale of the training data.

At block S305, training the convolutional neural network architecturewith the historical image blocks to create a tumor detection model.

In the embodiment, the convolutional neural network architectureincludes, but is not limited to, ResNet, AlexNet, VGG, Inception, and soon.

It can be understood that the block S303 and the block S304 can beomitted.

FIG. 4 illustrates a flowchart of an embodiment of a tumor detectingmethod. The tumor detecting method can begin at block S401.

At block S401, obtaining a detection image.

In the embodiment, the detection image can be thin-section detectionimage of the lung with high resolution. The detection image can also bethin-section detection image with high resolution of kidney, stomach, orthe like.

At block S402, segmenting the detection image into a number of detectionimage blocks according to the input size of the training data of theconvolutional neural network architecture, before segmenting, eachdetection image block comprising coordinate values.

In the embodiment, the method further includes: determining coordinatevalues of each detection image block before segmenting. The methodsegments the detection image into a number of detection image blockssatisfying the input size of the training data according to the inputsize of the training data of the convolutional neural networkarchitecture, coordinate values of each detection image block beingdetermined before segmenting, each detection image block partiallyoverlapping one or more of adjacent detection image block.

In the embodiment, the detection image block is square. The detectionimage block can be any other suitable shape, for example rectangle, orthe like. Each detection image block overlaps a half size of alongitudinally adjacent one of the detection image blocks and a halfsize of a widthwise adjacent one of the detection image blocks, and aquarter size of a diagonally adjacent one of the detection image blocks.

It can be understood that, in other embodiments, each detection imageblock overlaps a quarter size of the longitudinally adjacent one of thedetection image blocks or the widthwise adjacent one of the detectionimage blocks.

At block S403, inputting the detection image blocks to a preset tumordetection model to generate the image blocks of a result of thedetection images.

In the embodiment, the method includes inputting the detection imageblocks to a preset tumor detection model created at block S305 togenerate the image blocks of the result of the detection images.

At block S404, merging the image blocks of the result of detectionimages into a single image according to the coordinate values of eachdetection image block.

At block S405, generating a result of detection according to the colorsin the single image displayed on the display unit.

In the embodiment, the single image displays normal areas, abnormalareas, and overlapping areas of the abnormal areas in different colorson the display unit. The method judges and generates the result ofdetection according to the color depths in the single image displayed onthe display unit.

FIG. 5 illustrates the merging of a number of image blocks into a singleimage of result of detection. The four image blocks of the result ofdetection images 210 are merged into a single image 220 according to thecoordinate values of each detection image block. In the embodiment, theshape of the detection image block is same as the shape of the imageblocks of the result of detection image 210. Each detection image blockis square. Each detection image block overlaps a half size of thelongitudinally adjacent one of the detection image blocks and a halfsize of the widthwise adjacent one of the detection image blocks, and aquarter size of the diagonally adjacent one of the detection imageblocks.

As shown in FIG. 5, the image block of the result of detection images210 displays the normal area and the abnormal area in different color onthe display unit. In detail, the four image blocks of the result ofdetection image 210 display normal area in white and display abnormalarea in gray on the display unit. To generate the single image 220,adjacent image blocks of the result of detection image 210 are partiallyoverlapped. The single image 220 displays the normal areas in white, theoverlapping area of normal areas in white, the abnormal areas in gray,the overlapping area of one abnormal area and three normal areas ingray, the overlapping area of two or three abnormal areas and one or twonormal areas in dark gray, and the overlapping area of four abnormalareas in dark. To analyze the single image 220, the result of detectionis generated according to the color depths of the overlapped areadisplayed on the display unit. For example, the dark gray is the placewhere the tumor is spreading, and the tumor is in the dark place.

It can be understood that, the single image 220 displays the normalareas, the abnormal areas, and the overlapping areas of the abnormalareas in different colors on the display unit. The method judges andgenerates the result of detection according to the color depths in thesingle image displayed on the display unit.

It can be understood that, in other embodiments, the number of detectionimage blocks are greater than four.

It can be understood that, in other embodiments, a number of detectionimages can be employed. The detection images can be merged into a wholeimage. The whole image can display the whole lung. A number of images ofthe result of detection can be generated according to the detectionimages and the tumor detection model. The images of the result ofdetection can be merged into an overall image, and the result ofdetection of the lung can be determined according to the overall image.

The tumor detection device and method, and non-transitory storage mediumsegments the detection image into a number of detection image blocksaccording to the input size of the training data of the convolutionalneural network architecture. Then, the detection image blocks areinputted to the tumor detection model to generate the image blocks ofthe result of detection images. Next, the image blocks of the result ofdetection images are merged into a single image to generate a result ofdetection according to the coordinate values of each detection imageblock. Thus, the existence of a tumor and the accurate location of thetumor can be known according to the result of detection. The detectionspeed and the detection accuracy are improved.

It should be emphasized that the above-described embodiments of thepresent disclosure, including any particular embodiments, are merelypossible examples of implementations, set forth for a clearunderstanding of the principles of the disclosure. Many variations andmodifications can be made to the above-described embodiment(s) of thedisclosure without departing substantially from the spirit andprinciples of the disclosure. All such modifications and variations areintended to be included herein within the scope of this disclosure andprotected by the following claims.

What is claimed is:
 1. A tumor detection device comprising: a displayunit; a storage system; at least one processor; and the storage systemstoring one or more programs, which when executed by the at least oneprocessor, causing the at least one processor to: obtain one or moredetection images; segment the detection images into a plurality ofdetection image blocks according to an input size of training data of aconvolutional neural network architecture, before segmenting, each ofthe plurality of detection image blocks comprising coordinate values;input the detection image blocks to a preset tumor detection model togenerate a plurality of image blocks of a result of the detectionimages; merge the plurality of image blocks of the result of detectionimages into a single image according to the coordinate values of each ofthe detection image blocks, wherein the single image displays normalareas, abnormal areas, and overlapping areas of the abnormal areas indifferent colors on the display unit; generate a result of detectionaccording to color depths in the single image displayed on the displayunit.
 2. The tumor detection device as described in claim 1, wherein:each of the plurality of detection image blocks is rectangle, each ofthe detection image blocks partially overlaps with one or more ofadjacent detection image blocks.
 3. The tumor detection device asdescribed in claim 2, wherein: each of the detection image blocksoverlaps a half size of a longitudinally adjacent one of the detectionimage blocks and a half size of a widthwise adjacent one of thedetection image blocks, and a quarter size of a diagonally adjacent oneof the detection image blocks.
 4. The tumor detection device asdescribed in claim 1, further causing the at least one processor to:obtain one or more historical images; segment the historical images intoa plurality of historical image blocks according to the input size ofthe training data of the convolutional neural network architecture;train the convolutional neural network architecture with the historicalimage blocks to create the tumor detection model.
 5. The tumor detectiondevice as described in claim 4, after causing the at least one processorto segment the historical images into a plurality of historical imageblocks according to the input size of the training data of theconvolutional neural network architecture, further causing the at leastone processor to: delete one or more of the historical image blockscomprising backgrounds of more than 50% of an entire historical imageblock.
 6. The tumor detection device as described in claim 5, furthercausing the at least one processor to: expand a scale of the historicalimage blocks based on an image augmentation technology, the imageaugmentation technology comprising at least one of a group of imagedistortion, image flipping, image color and space changing, and imagetransformation.
 7. A tumor detection method applicable in a tumordetection device with a display unit, a storage system, and at least oneprocessor, comprising: the at least one processor obtaining one or moredetection images; the at least one processor segmenting the detectionimages into a plurality of detection image blocks according to an inputsize of training data of a convolutional neural network architecture,before segmenting, each of the plurality of detection image blockscomprising coordinate values; the at least one processor inputting thedetection image blocks to a preset tumor detection model to generate aplurality of image blocks of a result of the detection images; the atleast one processor merging the plurality of image blocks of the resultof detection images into a single image according to the coordinatevalues of each of the detection image blocks, wherein the single imagedisplays normal areas, abnormal areas, and overlapping areas of theabnormal areas in different colors on the display unit; the at least oneprocessor generating a result of detection according to color depths inthe single image displayed on the display unit.
 8. The tumor detectionmethod as described in claim 7, wherein the method comprises: the atleast one processor segmenting the detection images into a plurality ofrectangle detection image blocks according to an input size of trainingdata of a convolutional neural network architecture, each of thedetection image blocks partially overlapping with one or more ofadjacent detection image blocks.
 9. The tumor detection method asdescribed in claim 8, wherein: each of the detection image blocksoverlaps a half size of a longitudinally adjacent one of the detectionimage blocks and a half size of a widthwise adjacent one of thedetection image blocks, and a quarter size of a diagonally adjacent oneof the detection image blocks.
 10. The tumor detection method asdescribed in claim 7, wherein the method further comprises: the at leastone processor obtaining one or more historical images; the at least oneprocessor segmenting the historical images into a plurality ofhistorical image blocks according to the input size of the training dataof the convolutional neural network architecture; the at least oneprocessor training the convolutional neural network architecture withthe historical image blocks to create the tumor detection model.
 11. Thetumor detection method as described in claim 10, after the at least oneprocessor segmenting the historical images into a plurality ofhistorical image blocks according to the input size of the training dataof the convolutional neural network architecture, wherein the methodfurther comprises: the at least one processor deleting one or more ofthe historical image blocks comprising backgrounds of more than 50% ofan entire historical image block.
 12. The tumor detection method asdescribed in claim 11, wherein the method further comprises: the atleast one processor expanding a scale of the historical image blocksbased on an image augmentation technology, the image augmentationtechnology comprising at least one of a group of image distortion, imageflipping, image color and space changing, and image transformation. 13.A non-transitory storage medium storing a set of instructions, when theinstructions being executed by at least one processor of a tumordetection device, causing the at least one processor to: obtain one ormore detection images; segment the detection images into a plurality ofdetection image blocks according to an input size of training data of aconvolutional neural network architecture, before segmenting, each ofthe plurality of detection image blocks comprising coordinate values;input the detection image blocks to a preset tumor detection model togenerate a plurality of image blocks of a result of the detectionimages; merge the plurality of image blocks of the result of detectionimages into a single image according to the coordinate values of each ofthe detection image blocks, wherein the single image displays normalareas, abnormal areas, and overlapping areas of the abnormal areas indifferent colors on the display unit; generate a result of detectionaccording to color depths in the single image displayed on the displayunit.
 14. The non-transitory storage medium as described in claim 13,wherein: each of the plurality of detection image blocks is rectangle,each of the detection image blocks partially overlaps with one or moreof adjacent detection image blocks.
 15. The non-transitory storagemedium as described in claim 14, wherein: each of the detection imageblocks overlaps a half size of a longitudinally adjacent one of thedetection image blocks and a half size of a widthwise adjacent one ofthe detection image blocks, and a quarter size of a diagonally adjacentone of the detection image blocks.
 16. The non-transitory storage mediumas described in claim 13, further causing the at least one processor to:obtain one or more historical images; segment the historical images intoa plurality of historical image blocks according to the input size ofthe training data of the convolutional neural network architecture;train the convolutional neural network architecture with the historicalimage blocks to create the tumor detection model.
 17. The non-transitorystorage medium as described in claim 16, causing the at least oneprocessor to segment the historical images into a plurality ofhistorical image blocks according to the input size of the training dataof the convolutional neural network architecture, further causing the atleast one processor to: delete one or more of the historical imageblocks comprising backgrounds of more than 50% of an entire historicalimage block.
 18. The non-transitory storage medium as described in claim17, further causing the at least one processor to: expand a scale of thehistorical image blocks based on an image augmentation technology, theimage augmentation technology comprising at least one of a group ofimage distortion, image flipping, image color and space changing, andimage transformation.